ABSTRACT
Multiparametric indices offer a more comprehensive approach to voice quality assessment by taking into account multiple acoustic parameters. Artificial intelligence technology can be utilized in healthcare to evaluate data and optimize decision-making processes. Mobile devices provide new opportunities for remote speech monitoring, allowing the use of basic mobile devices as screening tools for the early identification and treatment of voice disorders. However, it is necessary to demonstrate equivalence between mobile device signals and gold standard microphone preamplifiers. Despite the increased use and availability of technology, there is still a lack of understanding of the impact of physiological, speech/language, and cultural factors on voice assessment. Challenges to research include accounting for organic speech-related covariables, such as differences in conversing voice sound pressure level (SPL) and fundamental frequency (f0), recognizing the link between sensory and experimental acoustic outcomes, and obtaining a large dataset to understand regular variation between and within voice-disordered individuals. Our study investigated the use of cellphones to estimate the Acoustic Voice Quality Index (AVQI) in a typical clinical setting using a Pareto-optimized approach in the signal processing path. We found that there was a strong correlation between AVQI results obtained from different smartphones and a studio microphone, with no significant differences in mean AVQI scores between different smartphones. The diagnostic accuracy of different smartphones was comparable to that of a professional microphone, with optimal AVQI cut-off values that can effectively distinguish between normal and pathological voice for each smartphone used in the study. All devices met the proposed 0.8 AUC threshold and demonstrated an acceptable Youden index value.
ABSTRACT
Recent work has shown the potential of using speech signals for remote detection of coronavirus disease 2019 (COVID-19). Due to the limited amount of available data, however, existing systems have been typically evaluated within the same dataset. Hence, it is not clear whether systems can be generalized to unseen speech signals and if they indeed capture COVID-19 acoustic biomarkers or only dataset-specific nuances. In this paper, we start by evaluating the robustness of systems proposed in the literature, including two based on hand-crafted features and two on deep neural network architectures. In particular, these systems are tested across two international COVID-19 detection challenge datasets (COMPARE and DICOVA2). Experiments show that the performance of the explored systems degraded to chance levels when tested on unseen data, especially those based on deep neural networks. To increase the generalizability of existing systems, we propose a new set of acoustic biomarkers based on speech modulation spectrograms. The new biomarkers, when used to train a simple linear classifier, showed substantial improvements in cross-dataset testing performance. Further interpretation of the biomarkers provides a better understanding of the acoustic properties of COVID-19 speech. The generalizability and interpretability of the selected biomarkers allow for the development of a more reliable and lower-cost COVID-19 detection system.
ABSTRACT
COVID-19 virus;has dragged the world into an epidemic that has infected more than 413 million people and caused the death of nearly 6 million people. Although biomedical tests provide the diagnosis of COVID-19 with high accuracy in the diagnosis of the disease, it increases the risk of infection due to the fact that it is a method that requires contact. Machine learning models have been proposed as an alternative to biomedical testing. Cough has been identified by the World Health Organization as one of the symptoms of COVID-19 disease. In this study, the success performance of the positive case situation with machine learning was examined using the COUGHVID dataset with cough voice recordings. In order to increase the performance of the model, MFCC, Δ-MFCC and Mel Coefficients attributes were obtained after preprocessing the sound recordings. In the ensemble learning model, features were used as independent variables and a value of 0.65 AUC-ROC was reached. In addition to these performance-enhancing changes, since the acoustic properties of male and female cough sounds are different, the training of persons was carried out separately from each other, and AUC-ROC values of 0.70 for females and 0.68 for males were obtained. Trimming the silent regions at the beginning and end of the recordings, using the ensemble learning model, and grouping based on gender provided better results for this study compared to previous studies. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic has changed people's habits, causing them to use large amounts of disposable items and exacerbating the already existing issue of pollution. One way to reduce the environmental impact of this shift in daily habits is to recycle these items, e.g. surgical masks that are the most common personal protective equipment against the virus, to produce panels for building applications. In this work, both the thermal and acoustical performance of such panels are evaluated using a small and a large scale investigation under real-world conditions. Small scale thermal tests are performed by means of the Hot Disk instrument while the acoustic investigations are performed by means of the impedance tube. Large scale tests are carried out in a reverberation chamber assessing both the heat flow passing through the wall and the acoustic absorption coefficient of the panels. Finally, the environmental impact of the innovative recycled panel is also investigated in a life cycle perspective. Overall, the material behavior scored well on these tests, suggesting that the proposed approach may be a good recycling method.
ABSTRACT
Unlike the term sound insulation, which means reducing the penetration of noise into other areas, sound absorption means reducing the reflection and energy of the sound on the surface. It has become a highly noticed issue in recent years because the noise in our daily life is increasing day by day, and it causes some health and comfort disorders. In many areas, textiles have been used for acoustics control and noise absorption purposes. The purpose of this work is to determine the most effective media for sound absorption performance and its relation to thermal conductivity from needle-punched nonwoven, meltblown nonwoven and hybrid forms in different arrangements of these fabrics. To provide comparable samples, both needle-punched nonwoven and meltblown nonwoven samples were produced from 100% Polypropylene fibres. According to sound absorption tests, the hybrid-structured sample having a composition similar to the needle-punched nonwoven sample placed at the bottom of our study, while the meltblown nonwoven sample placed as a face layer outperformed the rest of the samples in terms of sound absorption and thermal conductivity. ‘Meltblown only’ samples had remarkably higher sound absorption efficiency than most of the samples, while the ‘needle-punched nonwoven only’ sample had the lowest sound absorption efficiency in all frequencies.
ABSTRACT
The COVID-19 pandemic has led to a dramatic increase in the use of face masks. Face masks can affect both the acoustic properties of the signal and the speech patterns and have undesirable effects on automatic speech recognition systems as well as on forensic speaker recognition and identification systems. This is because the masks introduce both intrinsic and extrinsic variability into the audio signals. Moreover, their filtering effect varies depending on the type of mask used. In this paper we explore the impact of the use of different masks on the performance of an automatic speaker recognition system based on Mel Frequency Cepstral Coefficients to characterise the voices and on Support Vector Machines to perform the classification task. The results show that masks slightly affect the classification results. The effects vary depending on the type of mask used, but not as expected, as the results with FPP2 masks are better than those with surgical masks. An increase in speech intensity has been found with the FPP2 mask, which is related to the increased vocal effort made to counteract the effects of hearing loss. © 2022, Springer Nature Switzerland AG.
ABSTRACT
This paper presents the results of an experimental study on the acoustic efficiency of plastic surgical face masks. Since the very high number of disposable masks being used globally on a daily basis to face the Covid19 pandemic is posing new environmental risks, mainly connected to improper disposal, any possible improvements in the management of this waste stream is very important. In this work their potential use as sound porous absorber is discussed. Surgical face masks are mainly made of polypropylene fibers which show good acoustical properties. Their porous structure was studied through the measurement of some non-acoustic properties: bulk density, fiber diameter, porosity, flow resistivity and tortuosity. Moreover, the sound absorption performance of samples, made of scrapped face masks, with different thicknesses was evaluated using an impedance tube according to ISO 10534-2. The results obtained from the sound absorption spectra and two single indexes, Noise Reduction Coefficient and Sound Absorption Average showed a high sound absorption value over a frequency range of interest. Finally, the sound absorption spectra obtained for surgical face masks were compared with those obtained for fibrous materials currently used in building sector, suggesting that this fibrous waste could act as a possible substitute to traditional ones.